Abstract: Examples are described herein for facilitating responses to computing device service requests. In various examples, natural language processing may be performed on a plurality of incoming computing device service requests associated with a plurality of computing devices. A plurality of embeddings corresponding to the plurality of computing device service requests may be generated based on the natural language processing. Based on distances between each of a particular subset of the embeddings in an embedding space, a trending service issue associated with the computing device service requests corresponding to the particular subset may be predicted. The trending service issue may be provided to a computing device servicer associated with resolving a new incoming computing device service request.

Abstract: A server may include a receiving unit to obtain a set of battery attributes associated with a battery of a client device and a prediction unit to predict a battery condition by applying at least one first machine learning model to the set of battery attributes. The battery condition may include battery swelling, battery memory effect, battery performance degradation, or any combination thereof. Further, the server may include a recommendation unit to apply a second machine learning model to the predicted battery condition to predict a remaining life of the battery and recommend an action to be performed based on the predicted remaining life of the battery.

Abstract: A device with automated device temperature control is described. In one example, the device includes a processor and a weather forecast engine coupled to the processor. The weather forecast engine obtains weather data of a geographical location in which the device is located. The weather data includes values of environmental parameters. The weather data is then shared with a prediction engine. The device further includes a control engine coupled to the processor. The control engine receives a first threshold operating temperature determined for the device based on the weather data, a device model, and a mapping table from the prediction engine. The control engine then initiates a temperature control device, connected to the device, to cool the device if a current device temperature of the device is greater than the first threshold operating temperature.

Abstract: In one example in accordance with the present disclosure, an electronic device is described. An example electronic device includes a processor and memory storing executable instructions that when executed cause the processor to determine availability of memory resources and processing resources of multiple computing devices. The instructions also cause the processor to form a computing cluster based on the availability of the memory resources and the processing resources. The instructions further cause the processor to assign a computing task to the computing cluster to replace a cloud service.

Abstract: In some examples, resource availability prediction can include a non-transitory machine-readable medium having instructions thereon executable to receive at the first processing resource, the memory resource, or both, a plurality of input data from a plurality of sources comprising a first plurality of sensors to monitor a plurality of workstations and write from the first processing resource to the memory resource the received input data. The instructions can be executable to identify at the first processing resource or a second processing resource, output data representative of a resource availability prediction based on input data representative of the written data, receive from a mobile device accessible by a user, a request for the resource availability prediction, and transmit the output data to the mobile device via signaling sent via a radio in communication with a third processing resource of the mobile device.

Abstract: Aspects of the disclosure include an electronic device, comprising a processor. The processor is to receive a request to allocate computing resources, the request indicating a content type to use with the computing resources, determine available computing resources, determine a scoring of the computing resources according to the content type, and allocate a portion of the available computing resources according to the request, the content type, and the scoring.

Abstract: An example of an electronic device includes a processor to collect data of an input/output (I/O) interface of a docking station to which the electronic device is to couple, use at least one artificial intelligence (AI) processing model to process the collected data of the I/O interface to provide an AI processing model result that calculates past usage and predicts future usage of the I/O interface, and determine an estimated health of the docking station based on the AI processing model result.

Abstract: Examples are described herein for facilitating responses to computing device service requests. In various examples, natural language processing may be performed on a plurality of incoming computing device service requests associated with a plurality of computing devices. A plurality of embeddings corresponding to the plurality of computing device service requests may be generated based on the natural language processing. Based on distances between each of a particular subset of the embeddings in an embedding space, a trending service issue associated with the computing device service requests corresponding to the particular subset may be predicted. The trending service issue may be provided to a computing device servicer associated with resolving a new incoming computing device service request.

Abstract: A device with automated device temperature control is described. In one example, the device includes a processor and a weather forecast engine coupled to the processor. The weather forecast engine obtains weather data of a geographical location in which the device is located. The weather data includes values of environmental parameters. The weather data is then shared with a prediction engine. The device further includes a control engine coupled to the processor. The control engine receives a first threshold operating temperature determined for the device based on the weather data, a device model, and a mapping table from the prediction engine. The control engine then initiates a temperature control device, connected to the device, to cool the device if a current device temperature of the device is greater than the first threshold operating temperature.